public MatchingPair MapMatch(LostAndFoundIndexedItem loss, LostAndFoundIndexedItem finding)
{
var pair = Map(loss, finding);
pair.PercentMatch = 1;
return(pair);
}
public MatchingPair Map(LostAndFoundIndexedItem loss, LostAndFoundIndexedItem finding)
{
var pair = new MatchingPair
{
LossDateOfIncident = MapDate(loss.DateOfIncident),
FindingDateOfIncident = MapDate(finding.DateOfIncident),
/*LossEasyFindNo = MapIdentifier(loss.EasyFindNo),
* FindingEasyFindNo = MapIdentifier(finding.EasyFindNo),
* LossBagTag = MapIdentifier(loss.BagTag),
* FindingBagTag = MapIdentifier(loss.BagTag),*/
// TODO:
// Description
// LossLocation
};
pair.LossCategory = MapCategory(loss.CategoryID, loss.SubCategoryID);
pair.FindingCategory = MapCategory(finding.CategoryID, finding.SubCategoryID);
pair.LossColors = MapColors(loss.Attributes.OfType <ColorValueAttribute>());
pair.FindingColors = MapColors(finding.Attributes.OfType <ColorValueAttribute>());
//*
MapBool(pair.LossAttributes, loss.Attributes.OfType <BoolAttribute>());
MapBool(pair.FindingAttributes, finding.Attributes.OfType <BoolAttribute>());
//*/
MapDouble(pair.LossAttributes, loss.Attributes.OfType <DoubleAttribute>());
MapDouble(pair.FindingAttributes, finding.Attributes.OfType <DoubleAttribute>());
pair.LossMoney = MapMoney(loss.Attributes.OfType <MoneyValueAttribute>());
pair.FindingMoney = MapMoney(finding.Attributes.OfType <MoneyValueAttribute>());
MapInteger(pair.LossAttributes, loss.Attributes.OfType <IntegerAttribute>());
MapInteger(pair.FindingAttributes, finding.Attributes.OfType <IntegerAttribute>());
return(pair);
}
public static void Main(string[] args)
{
if (args.Length != 4)
{
Console.WriteLine("Usage: NeuralDemo.exe network.dat tree.dat training.json test.json reindex.json");
}
var networkFileame = args[0];
var treeFilename = args[1];
var trainingFilename = args[2];
var testFilename = args[3];
var reindexFilename = args[4];
XmlConfigurator.ConfigureAndWatch(new FileInfo("log4net.config"));
var stopWatch = new Stopwatch();
stopWatch.Start();
var network = Network.Load(networkFileame);
stopWatch.Stop();
Console.WriteLine("Network loaded in {0}.", stopWatch.Elapsed);
stopWatch.Restart();
DecisionTree tree;
using (var fileStream = File.OpenRead(treeFilename))
tree = Serializer.Load <DecisionTree>(fileStream);
stopWatch.Stop();
Console.WriteLine("DecisionTree loaded in {0}.", stopWatch.Elapsed);
stopWatch.Restart();
var deserializer = new ReindexDeserializer(reindexFilename);
var data = deserializer.Deserialize();
stopWatch.Stop();
Console.WriteLine("Data loaded in {0}.", stopWatch.Elapsed);
stopWatch.Restart();
var mapperSettings = ItemMapperSettings.FromDeserialized(data);
var mapper = new MatchedItemsMapper(mapperSettings);
stopWatch.Stop();
Console.WriteLine("Mapper loaded in {0}.", stopWatch.Elapsed);
/*stopWatch.Restart();
* //var loader = new BinaryDataLoader(reindexFilename, trainingFilename, testFilename);
* //var loader = new JsonDataLoader(reindexFilename, false, false, false);
* var loader = new JsonPairDataLoader(reindexFilename, trainingFilename, testFilename);
* var learningData = loader.Load();
* stopWatch.Stop();
* Console.WriteLine("Pairs loaded in {0}", stopWatch.Elapsed);*/
var items = data.Items.Where(item => item.ItemType == LostAndFoundIndexedItem.Type.Loss).ToArray();
Console.WriteLine("Ready.");
Console.WriteLine();
while (true)
{
Console.WriteLine("Enter some data on the item you found:");
var date = EnterData("Date of finding");
var money = EnterData("Money");
var attributeList = new List <AttributeBase>();
if (money != null)
{
attributeList.Add(new MoneyValueAttribute {
Value = new MoneyValue {
Currency = "EUR", Value = Convert.ToDecimal(money)
}
});
}
var color = EnterData("Color (#HTML-Code)");
if (color != null)
{
attributeList.Add(new ColorValueAttribute {
Value = color
});
}
var finding = new LostAndFoundIndexedItem
{
CategoryID = "ff61ce82-db05-b000-07ff-0000000000" + EnterData("CategoryID (ff61ce82-db05-b000-07ff-0000000000XX)"),
SubCategoryID = "b988f940-db05-b000-07ff-0000000000" + EnterData("SubCategoryID (b988f940-db05-b000-07ff-0000000000XX)"),
DateOfIncident = date != null?Convert.ToDateTime(date) : new DateTime(1999, 12, 31),
Attributes = attributeList,
};
var results = new List <Tuple <LostAndFoundIndexedItem, MatchingPair, int> >();
Parallel.ForEach(items, item =>
{
var pair = mapper.Map(item, finding);
pair.PercentMatch = network.Compute(pair.ToVectorArray(new Dictionary <string, IndexableAttributeMetadata>())).Single();
var decisionTreeResult = tree.Decide(pair.ToVectorArray(new Dictionary <string, IndexableAttributeMetadata>()));
lock (results)
{
results.Add(new Tuple <LostAndFoundIndexedItem, MatchingPair, int>(item, pair, decisionTreeResult));
}
});
Console.WriteLine("Top 5 Neural Network Results:");
Console.WriteLine(string.Join(Environment.NewLine,
results.OrderByDescending(result => result.Item2.PercentMatch)
.Take(5)
.Select(result => string.Format("{0}%\t{1}", result.Item2.PercentMatch * 100, result.Item1.RecordNumber))));
Console.WriteLine("Decision Tree Results:");
Console.WriteLine(string.Join(Environment.NewLine,
results.Where(result => result.Item3 == 1)
.Select(result => string.Format("{0}%\t{1}", result.Item3 * 100, result.Item1.RecordNumber))));
Console.Write("Enter next item? [Y/n]");
var next = Console.ReadLine();
if (!string.IsNullOrEmpty(next) && next.ToLower() != "y")
{
return;
}
}
}
